Conventionally, an image processing apparatus which converts input multi-grayscale image data into binary image data and outputs the binary image data adopts, e.g., error diffusion as a method of converting a multi-grayscale image into a binary image. With this error diffusion, a quantization error between the grayscale value of a pixel of interest, and a fixed binarization threshold value used to binarize this grayscale value is diffused to the grayscale values of neighboring pixels of the pixel of interest, and these grayscale values are sequentially binarized.
However, when a local image which suffers a large density change in a multi-valued image is to be binarized by error diffusion, the following phenomena are observed in a quantized image. Basically, a quantization method using error diffusion is based on the principle of diffusing the density value of a pixel to neighboring pixels. For this reason, in an image having a change portion from a high density to low density and vice versa, which often appear in characters, edges, and the like, dot generation after binarization is delayed. This means that the dot positions of the quantized image become different from those in the multi-valued image. For this reason, a phenomenon called “sweeping” occurs in a change portion from the high density to low density. “Sweeping” indicates the absence of dots at positions where they should be actually present. That is, in case of a binary image in which the density value of a white pixel is expressed by 0 and that of a black pixel is expressed by 1, the density value of a pixel whose density value must be 1 becomes 0 due to delay of dot generation and distribution of quantization errors. Contrary to sweeping, phenomena such as crush in shadow, disruption, and the like of an image occur in a change portion from the low density to high density since the density value of a pixel, whose pixel value must be 0 becomes 1. With these phenomena, when a multi-valued image is quantized using error diffusion, density reproducibility often impairs. As a result, when such image is displayed or printed, it is reproduced as an image so-called “pseudo contour”.
In a multi-valued image having a specific density pattern, stripe patterns so-called “texture” and “worm” are generated in a reproduced image. Such phenomena often adversely influence the visual impression of a reproduced image.
To solve these problems, U.S. Pat. No. 5,553,166 discloses a method (to be referred to as a first method hereinafter) for solving a problem of delay of dot generation of a pixel of interest by varying a binarization threshold value in accordance with the grayscale value of image data of the pixel of interest so that the threshold value is decreased when the grayscale value (density value) of input image data is small and vice versa.
Also, Japanese Patent Laid-Open No. JP09247450A has proposed a method (to be referred to as a second method hereinafter) for preventing generation of “texture”, “worm”, and the like by setting a binarization threshold value and diffusion coefficients on the basis of a feature amount such as a change in gradation and the like in a pixel of interest and its surrounding pixels.
However, the first method requires complicated arithmetic operations and discrimination circuits, look-up tables, or the like. Also, a complicated tuning process is required to set an optimal threshold value so as to obtain high image quality. The first method produces nearly no effects in terms of suppression of generation of “texture”, “worm”, and the like.
Since the second method uses filters based on a human visual system upon calculation of a feature amount, complicated arithmetic operations and discrimination processes are required. Furthermore, the second method has nearly no effect of solving problems of “sweeping” and “pseudo contour” due to disappearance of a dot pattern.